Before being entered into Epi Info 3.5.3 and exported to statistical package for Social Science (SPSS) 20, the data was cleaned, updated, and double-checked (IBM, USA). Frequency and percentage were used to describe the characteristics of the patients. The Pearson chi-square test was performed to examine the association among sociodemographic and topographic characteristics. Variables with a p value less than 0.25 were selected as candidates for the multivariable analysis and fitted into a logistic regression model in the bivariable analysis. A statistically significant association was confirmed at a p value of <0.05.
Epi info 3
Manufactured by IBM
Sourced in United States
Epi Info 3.5.1 is a public domain software package designed for the Microsoft Windows operating system. It is primarily used for the purpose of data collection and analysis, with a focus on epidemiological studies and public health investigations.
Automatically generated - may contain errors
Lab products found in correlation
Spss version 20, by IBM (2 mentions)
Spss version 16, by IBM (2 mentions)
Spss 20, by IBM (1 mentions)
Spss 21, by IBM (1 mentions)
Spss statistics for windows version 20, by IBM (1 mentions)
Spss statistical software package, by IBM (1 mentions)
Spss statistics for windows version 26, by IBM (1 mentions)
10 protocols using epi info 3
1
Sociodemographic and Topographic Characteristics Analysis
2
Assessing Knowledge, Attitude, and Practice
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Data were managed and analyzed using Epi-Info 3.5.3 and SPSS 21.0 software (IBM Corp., Armonk, N.Y., USA). Frequencies and means (SD) were used for data description. Shapiro-Wilks test tested continuous variables for normal distribution. As data were normally distributed, t-test and ANOVA were used to examine the difference in mean knowledge, attitude, and practice scores according to gender, nationality, governorate, and level of education. Pearson correlation coefficient was used to evaluate the correlation between the scores and participants’ age and years of experience. The significance level used was p < 0.05.
3
Determinants of Cosmetic Adverse Events
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Descriptive statistics were used to summarize the nature and frequency of cosmetic use. Bivariate and multivariate logistic regression analyses were applied to investigate the determinants of cosmetic use associated with adverse events. All explanatory variables associated with the outcome variable in the bivariate analysis with p < 0.20 were included in the multivariate logistic regression model. Epi info 3.5.1 and SPSS v.20 for Windows prram were used for the data entry and analysis, respectively. Statistical significance was set at p < 0.05.
4
Statistical Analysis of Epidemiological Data
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The collected data were checked, coded and entered to epidemiological information package (EPi-info 3.5.1) and exported to statistical package for social sciences (SPSS) version 20 for further analysis. For most variables, data were presented as frequencies and percentages. Univariate logistic regression analysis was used primarily to check which independent variables are associated with the dependent variable individually. Variables found to have association (p < 0.2) with the dependent variables were then analyzed by multiple logistic regression for controlling the possible effect of confounders and finally the variables which had significant association were identified on the basis of AOR with 95 % CI and p< 0.05.
5
Factors Associated with Vernal Keratoconjunctivitis
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The data were entered into EPI INFO 3.5.1 and SPSS version 20 was used for analysis. Frequencies and cross tabulation were used to check consistency. Bivariate logistic regression was used to determine the association between VKC and independent variables indicating the crude odds ratio. Crude odds ratio was calculated to show the strength of association between the outcome and single independent variables. Variables with P-value less than 0.2 at bivariate logistic regression were included in a multivariate logistic regression model to determine VKC risk factors adjusted for potential confounders. Adjusted odds ratio, 95 % CI and two sided p-value were calculated. A p-value less than 0.05 was considered statistically significant.
6
Evaluation of Pre-Post Intervention
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The collected data was checked for completeness and consistency on daily basis by the immediate supervisor and then manually edited and coded to ensure data quality. Then data was entered into a computer and analyzed using Epi-Info 3.5.1 and SPSS version 20 software respectively.
Variables were recorded and initially descriptive statistics was calculated. Means and standard deviations were calculated for continuous variables while proportions and frequencies for categorical variables. The paired t-test was used to compare the changes in scores from pre- to post intervention and to assure the net gain. Alpha level of p < 0.05 was considered to be statistically significant. Tables, graphs, charts and texts were used for data presentation.
Variables were recorded and initially descriptive statistics was calculated. Means and standard deviations were calculated for continuous variables while proportions and frequencies for categorical variables. The paired t-test was used to compare the changes in scores from pre- to post intervention and to assure the net gain. Alpha level of p < 0.05 was considered to be statistically significant. Tables, graphs, charts and texts were used for data presentation.
7
Diagnostic Accuracy of Serological Tests
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The collected data were entered in Epi info 3.5 from CDC and exported to IBM SPSS version 16.0 (SPSS Inc. Chicago, USA). The sensitivities, specificities, positive predictive values, negative predictive values of the serological tests were calculated using MedCalc for windows, version 18.11.3 (MedCalc, Software, Ostend, Belgium). STARD 2015 guidelines for reporting diagnostic accuracy studies was strictly followed [8 (link)].
8
Evaluating Serological Test Accuracy
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The collected data were entered in Epi info 3.5 from CDC and exported to IBM SPSS version 16.0 (SPSS Inc. Chicago, USA). The sensitivities, specificities, positive predictive values, negative predictive values of the serological tests were calculated using MedCalc for windows, version 18.11.3 (MedCalc, Software, Ostend, Belgium). STARD 2015 guidelines for reporting diagnostic accuracy studies was strictly followed [8] .
9
Predictive Factors for Positive Outcomes
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All data were entered in to Epi Info 3.1 and analysed using SPSS statistical software package (IBM Corp. Released 2011. IBM SPSS Statistics for Windows, Version 20.0. Armonk, NY: IBM Corp.). Descriptive statistics was used to determine differences within the data of variables. All explanatory variables with a p value ≤0.2 in the bivariate analysis were included in the multivariate logistic regression model to identify independent predictive variables. Odds ratio (OR) and 95 % confidence intervals (CI) were calculated and the results were considered statistically significant at p < 0.05.
10
Medicinal Plants Usage Patterns Analysis
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The data were entered and cleaned using Epi Info 3.1 software and exported to IBM SPSS Statistics for Windows, version 26.0 (IBM Corp., Armonk, N.Y., USA) for further analysis. Descriptive statistics (frequency and percentage) were used for describing and summarizing the data.
The medicinal plants were recorded in Microsoft excel 2010 and tabulated in the table with their respective local name, families, parts used, dosage form, method of preparation, frequency of use, duration of use and source of plants. For all the traditional medicines described, Relative Frequency of Citation (RFC) was calculated using equation 1.25 (link)
1
Where FC is the number of respondents who mentioned the plant species and N is the number of respondent participated in the study.
The results of the RFC and the top 10 medicinal plants used were presented in the radar diagram.
The medicinal plants were recorded in Microsoft excel 2010 and tabulated in the table with their respective local name, families, parts used, dosage form, method of preparation, frequency of use, duration of use and source of plants. For all the traditional medicines described, Relative Frequency of Citation (RFC) was calculated using equation 1.25 (link)
1
Where FC is the number of respondents who mentioned the plant species and N is the number of respondent participated in the study.
The results of the RFC and the top 10 medicinal plants used were presented in the radar diagram.
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